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Protocols in ecological and environmental plant physiology

 

Article << Previous     |     Next >>   Contents Vol 61(5)

Specific leaf area: a predictive model using dried samples

Vania Torrez A B C F , Peter M. Jørgensen C and Amy E. Zanne D E

A Division of Plant Conservation and Population Biology, Department of Biology, University of Leuven, B-3001 Leuven, Belgium.
B Department of Biology, University of Missouri, St Louis, MO 63108, USA.
C Missouri Botanical Garden, PO Box 299, St Louis, MO 63166, USA.
D Department of Biological Sciences, George Washington University, Washington, DC 20052, USA.
E Center for Conservation and Sustainable Development, Missouri Botanical Garden, St Louis, MO 63166, USA.
F Corresponding author. Email: torflorvania@gmail.com

Australian Journal of Botany 61(5) 350-357 http://dx.doi.org/10.1071/BT12236
Submitted: 7 September 2012  Accepted: 8 May 2013   Published: 14 June 2013


 
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Abstract

Specific leaf area (SLA; fresh-leaf area/dry mass) describes the amount of leaf area for light capture per unit of biomass invested. The standard protocol is simple; however, it requires recently collected sun-exposed leaves to determine fresh-leaf area, limiting where and which samples can be studied. A protocol to predict SLA for fresh leaves from herbarium-dried leaves was developed from samples collected in a dry forest in Bolivia. Leaf area was measured both fresh and dried on the same leaf samples to generate two general mixed-effects models, varying in their inclusion of the position in the crown where the leaf developed. As a test of the potential generality of the models for other systems, we applied them to samples collected in an oak–hickory forest in Missouri, USA. Both models performed well. A recommended protocol for studies predicting SLA from dry leaves was developed. These predictive models and protocols can extend the temporal, geographic, ecological and taxonomic scope of SLA studies.

Additional keywords: Bolivia, dry forest, herbarium specimens, leaf traits, mixed-effects models.


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